Last updated: 2020-11-23

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``

1 introduction

this is a base project to simply show creating the supplementary data files and show casing how this simple concept can be used to create maintainable that is easily extendable and has the tools required to manage complexity.

to understand this document browse it as if you would any website; exploring is important and baked into it

1.1 my musings on managing complexity

anyone can write code a computer can understand; the real challenge is writing in a way that humans can

when a field becomes sufficiently advanced it reinvents algebra; so lets skip straight to algebra

we write software/code to manage complexities; without these tools it would be impossible to do much of all we do; but at the end of the day it is just a tool and picking the right tool for the job is important. when the only tool you have is a hammer everything looks like a nail below I will outline some tools that will help us manage the complexities of working in PGDP; especially remotely.

1.1.1 Tool #1 Rmarkdown

this document you are reading is a rmarkdown; it is magic as far as I am concerned it combines some powerful yet simple ideas but it ultimately allows you to communicate your purpose; documentation , logic , and code, work report, management tool, training document, research paper, data product, visualization tool, presentation material, panflet and hand out generator, report tool and many other uses in one manageable place; seriously just watch this. it is truly a marvel. more on literate programming here for more info but the jist is this

Literate programming is a programming paradigm introduced by Donald Knuth in which a computer program is given an explanation of its logic in a natural language, such as English, interspersed with snippets of macros and traditional source code, from which compilable source code can be generated.[1] The approach is used in scientific computing and in data science routinely for reproducible research and open access purposes.[2] Literate programming tools are used by millions of programmers today

rmarkdown gives you headings that collapse to further structure this, allowing you to jump between section very quickly

1.1.1.1 showing off what it can do in a static website

this here is a piece of code using a package called ggplot2; the data is from the world bank and is called gapminder it is a simple data set on life expectancy and gdp per country.

ggplot uses a grammar for describing visual components

library(ggplot2)
data(gapminder, package = "gapminder")
gg <- ggplot(gapminder, aes(gdpPercap, lifeExp, color = continent)) +
  geom_point(aes(size = pop, frame = year, ids = country)) +
  scale_x_log10()
Warning: Ignoring unknown aesthetics: frame, ids
plotly::ggplotly(gg)

1.1.1.2 adjusting the example above for our section

library(ggplot2)
library(tidyverse)
-- Attaching packages --------------------------------------- tidyverse 1.3.0 --
v tibble  3.0.3     v dplyr   1.0.2
v tidyr   1.1.2     v stringr 1.4.0
v readr   1.4.0     v forcats 0.5.0
v purrr   0.3.4     
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
pgdp <- cansim::get_cansim( "36-10-0402-02") %>% select(REF_DATE,GEO,Value,VALUE,`North American Industry Classification System (NAICS)`) %>% pivot_wider(names_from = Value, values_from = VALUE) 
Accessing CANSIM NDM product 36-10-0402 from Statistics Canada
Parsing data
Folding in metadata
#
gg <- ggplot(pgdp, aes(`Contributions to percent change`, `Current dollars`, color = GEO)) +
  geom_point(aes(size = `Chained (2012) dollars`, frame = REF_DATE, ids = `North American Industry Classification System (NAICS)`)) +
  scale_x_log10()
Warning: Ignoring unknown aesthetics: frame, ids
plotly::ggplotly(gg)
Warning in self$trans$transform(x): NaNs produced
Warning: Transformation introduced infinite values in continuous x-axis

use string matching to only 2 digit naics and manufacturing and bring in MSM data

1.2 Tool #2 git

git allows us to effectively track what we have worked on and go back if we need to; it also helps us keep track of versions and also enables to work collaboratively in a tractable and manageable way. scroll to the and click on the black box workflowr past versions; there you can see track all that I have done to this page.

2 technical notes

2.1 tools used

this project uses workflowr, gitand rmarkdown to ensure reproducibility; documentation and version history. alternatively it can be hosted on github or gitlab(on net a)

2.2

Describe your project. hell oworld

Notes on R

3 ## R libraries

R libraries are similar to SAS procs for example proc SQL roughly translates to the library dplyr in R

before we start working in R we need to install necessary R libraries and call them and set our working directory.

package installation is commented out since we do only need to install the libraries once

4 


sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)

Matrix products: default

locale:
[1] LC_COLLATE=English_Canada.1252  LC_CTYPE=English_Canada.1252   
[3] LC_MONETARY=English_Canada.1252 LC_NUMERIC=C                   
[5] LC_TIME=English_Canada.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4    
 [5] readr_1.4.0     tidyr_1.1.2     tibble_3.0.3    tidyverse_1.3.0
 [9] ggplot2_3.3.2   workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5        lubridate_1.7.9   ps_1.4.0          assertthat_0.2.1 
 [5] rprojroot_1.3-2   digest_0.6.25     R6_2.4.1          cellranger_1.1.0 
 [9] backports_1.1.10  reprex_0.3.0      evaluate_0.14     httr_1.4.2       
[13] pillar_1.4.6      rlang_0.4.7       curl_4.3          lazyeval_0.2.2   
[17] readxl_1.3.1      rstudioapi_0.11   data.table_1.13.2 whisker_0.4      
[21] blob_1.2.1        rmarkdown_2.5     labeling_0.4.2    htmlwidgets_1.5.2
[25] munsell_0.5.0     broom_0.7.2       compiler_4.0.3    httpuv_1.5.4     
[29] modelr_0.1.8      xfun_0.18         pkgconfig_2.0.3   htmltools_0.5.0  
[33] tidyselect_1.1.0  fansi_0.4.1       viridisLite_0.3.0 crayon_1.3.4     
[37] dbplyr_1.4.4      withr_2.3.0       later_1.1.0.1     grid_4.0.3       
[41] cansim_0.3.5      jsonlite_1.7.1    gtable_0.3.0      lifecycle_0.2.0  
[45] DBI_1.1.0         git2r_0.27.1      magrittr_1.5      scales_1.1.1     
[49] cli_2.1.0         stringi_1.5.3     farver_2.0.3      fs_1.5.0         
[53] promises_1.1.1    xml2_1.3.2        ellipsis_0.3.1    generics_0.0.2   
[57] vctrs_0.3.4       tools_4.0.3       glue_1.4.2        hms_0.5.3        
[61] crosstalk_1.1.0.1 yaml_2.2.1        colorspace_1.4-1  rvest_0.3.6      
[65] plotly_4.9.2.1    knitr_1.30        haven_2.3.1